The purpose of this study is to test the feasibility of segmentation analyses to predict energy balance in midlife women. Overweight and obesity are major risk factors for the development of type 2 diabetes in women in midlife. This is a critical time period for women because without regard for energy balance, there is the potential for age-related weight gain as basal metabolic rate decreases with age. Highly predictive theoretical approaches are not available for the prediction of energy balance that adequately address the interplay between social cognitive factors and situational factors in the context of the environment. Segmentation analyses based on attitudinal factors (social cognitive) across eating situations (situation/environment) can be used to subdivide a large heterogeneous group of midlife women into smaller homogeneous segments that allow for effective tailored interventions. Project aims are to: 1) use think aloud and focus group data to create two measurement instruments (attitudinal questionnaire and eating occasions diary), 2) use segmentation analyses based on data collected with these instruments to identify distinct segments of women based on their prevailing attitudes as determined by social cognitive factors (Attitudes Segments) and distinct eating situations (Needs States) driven by the rational and situational/emotional needs that underlie food choices, 3) measure energy balance (diet records, activity monitor and indirect calorimetry), and 4) use multiple regression analyses to determine whether segmentation by Attitudes Segments across Needs States is predictive of energy balance. Segmentation based on this matrix is an innovative strategy to better understand energy balance. The results can be used to develop tailored interventions that are highly relevant to specific segments of women resulting in maintenance of energy balance, thereby preventing overweight and associated risk for type 2 diabetes.